Pgvector 0.3.0
Prefix Reserveddotnet add package Pgvector --version 0.3.0
NuGet\Install-Package Pgvector -Version 0.3.0
<PackageReference Include="Pgvector" Version="0.3.0" />
paket add Pgvector --version 0.3.0
#r "nuget: Pgvector, 0.3.0"
// Install Pgvector as a Cake Addin #addin nuget:?package=Pgvector&version=0.3.0 // Install Pgvector as a Cake Tool #tool nuget:?package=Pgvector&version=0.3.0
pgvector-dotnet
pgvector support for .NET (C#, F#, and Visual Basic)
Supports Npgsql, Dapper, Entity Framework Core, and Npgsql.FSharp
Getting Started
Follow the instructions for your database library:
- C# - Npgsql, Dapper, Entity Framework Core
- F# - Npgsql.FSharp
- Visual Basic - Npgsql
Or check out an example:
- Embeddings with OpenAI
- Bulk loading with
COPY
Npgsql (C#)
Run
dotnet add package Pgvector
Create a connection
var dataSourceBuilder = new NpgsqlDataSourceBuilder(connString);
dataSourceBuilder.UseVector();
await using var dataSource = dataSourceBuilder.Build();
var conn = dataSource.OpenConnection();
Enable the extension
await using (var cmd = new NpgsqlCommand("CREATE EXTENSION IF NOT EXISTS vector", conn))
{
await cmd.ExecuteNonQueryAsync();
}
conn.ReloadTypes();
Create a table
await using (var cmd = new NpgsqlCommand("CREATE TABLE items (id serial PRIMARY KEY, embedding vector(3))", conn))
{
await cmd.ExecuteNonQueryAsync();
}
Insert a vector
await using (var cmd = new NpgsqlCommand("INSERT INTO items (embedding) VALUES ($1)", conn))
{
var embedding = new Vector(new float[] { 1, 1, 1 });
cmd.Parameters.AddWithValue(embedding);
await cmd.ExecuteNonQueryAsync();
}
Get the nearest neighbors
await using (var cmd = new NpgsqlCommand("SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", conn))
{
var embedding = new Vector(new float[] { 1, 1, 1 });
cmd.Parameters.AddWithValue(embedding);
await using (var reader = await cmd.ExecuteReaderAsync())
{
while (await reader.ReadAsync())
{
Console.WriteLine(reader.GetValue(0));
}
}
}
Add an approximate index
await using (var cmd = new NpgsqlCommand("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)", conn))
{
await cmd.ExecuteNonQueryAsync();
}
Use vector_ip_ops
for inner product and vector_cosine_ops
for cosine distance
See a full example
Dapper
Run
dotnet add package Pgvector.Dapper
Import the library
using Pgvector.Dapper;
Create a connection
SqlMapper.AddTypeHandler(new VectorTypeHandler());
var dataSourceBuilder = new NpgsqlDataSourceBuilder(connString);
dataSourceBuilder.UseVector();
await using var dataSource = dataSourceBuilder.Build();
var conn = dataSource.OpenConnection();
Enable the extension
conn.Execute("CREATE EXTENSION IF NOT EXISTS vector");
conn.ReloadTypes();
Define a class
public class Item
{
public int Id { get; set; }
public Vector? Embedding { get; set; }
}
Create a table
conn.Execute("CREATE TABLE items (id serial PRIMARY KEY, embedding vector(3))");
Insert a vector
var embedding = new Vector(new float[] { 1, 1, 1 });
conn.Execute(@"INSERT INTO items (embedding) VALUES (@embedding)", new { embedding });
Get the nearest neighbors
var embedding = new Vector(new float[] { 1, 1, 1 });
var items = conn.Query<Item>("SELECT * FROM items ORDER BY embedding <-> @embedding LIMIT 5", new { embedding });
foreach (Item item in items)
{
Console.WriteLine(item.Embedding);
}
Add an approximate index
conn.Execute("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)");
// or
conn.Execute("CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)");
Use vector_ip_ops
for inner product and vector_cosine_ops
for cosine distance
See a full example
Entity Framework Core
Run
dotnet add package Pgvector.EntityFrameworkCore
The latest version works with .NET 8. For .NET 6 and 7, use version 0.1.2 and this readme.
Import the library
using Pgvector.EntityFrameworkCore;
Enable the extension
protected override void OnModelCreating(ModelBuilder modelBuilder)
{
modelBuilder.HasPostgresExtension("vector");
}
Configure the connection
protected override void OnConfiguring(DbContextOptionsBuilder optionsBuilder)
{
optionsBuilder.UseNpgsql("connString", o => o.UseVector());
}
Define a model
public class Item
{
public int Id { get; set; }
[Column(TypeName = "vector(3)")]
public Vector? Embedding { get; set; }
}
Insert a vector
ctx.Items.Add(new Item { Embedding = new Vector(new float[] { 1, 1, 1 }) });
ctx.SaveChanges();
Get the nearest neighbors
var embedding = new Vector(new float[] { 1, 1, 1 });
var items = await ctx.Items
.OrderBy(x => x.Embedding!.L2Distance(embedding))
.Take(5)
.ToListAsync();
foreach (Item item in items)
{
if (item.Embedding != null)
{
Console.WriteLine(item.Embedding);
}
}
Also supports MaxInnerProduct
and CosineDistance
Get the distance
var items = await ctx.Items
.Select(x => new { Entity = x, Distance = x.Embedding!.L2Distance(embedding) })
.ToListAsync();
Get items within a certain distance
var items = await ctx.Items
.Where(x => x.Embedding!.L2Distance(embedding) < 5)
.ToListAsync();
Add an approximate index
protected override void OnModelCreating(ModelBuilder modelBuilder)
{
modelBuilder.Entity<Item>()
.HasIndex(i => i.Embedding)
.HasMethod("hnsw")
.HasOperators("vector_l2_ops")
.HasStorageParameter("m", 16)
.HasStorageParameter("ef_construction", 64);
// or
modelBuilder.Entity<Item>()
.HasIndex(i => i.Embedding)
.HasMethod("ivfflat")
.HasOperators("vector_l2_ops")
.HasStorageParameter("lists", 100);
}
Use vector_ip_ops
for inner product and vector_cosine_ops
for cosine distance
See a full example
Npgsql.FSharp
Run
dotnet add package Pgvector
Import the library
open Pgvector
Create a connection
let dataSourceBuilder = new NpgsqlDataSourceBuilder(connString)
dataSourceBuilder.UseVector()
use dataSource = dataSourceBuilder.Build()
Enable the extension
dataSource
|> Sql.fromDataSource
|> Sql.query "CREATE EXTENSION IF NOT EXISTS vector"
|> Sql.executeNonQuery
Create a table
dataSource
|> Sql.fromDataSource
|> Sql.query "CREATE TABLE items (id serial PRIMARY KEY, embedding vector(3))"
|> Sql.executeNonQuery
Insert a vector
let embedding = new Vector([| 1f; 1f; 1f |])
let parameter = new NpgsqlParameter("", embedding)
dataSource
|> Sql.fromDataSource
|> Sql.query "INSERT INTO items (embedding) VALUES (@embedding)"
|> Sql.parameters [ "embedding", Sql.parameter parameter ]
|> Sql.executeNonQuery
Get the nearest neighbors
type Item = {
Id: int
Embedding: Vector
}
dataSource
|> Sql.fromDataSource
|> Sql.query "SELECT * FROM items ORDER BY embedding <-> @embedding LIMIT 5"
|> Sql.parameters [ "embedding", Sql.parameter parameter ]
|> Sql.execute (fun read ->
{
Id = read.int "id"
Embedding = read.fieldValue<Vector> "embedding"
})
|> printfn "%A"
Add an approximate index
dataSource
|> Sql.fromDataSource
|> Sql.query "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)"
|> Sql.executeNonQuery
Use vector_ip_ops
for inner product and vector_cosine_ops
for cosine distance
See a full example
Npgsql (Visual Basic)
Run
dotnet add package Pgvector
Create a connection
Dim dataSourceBuilder As New NpgsqlDataSourceBuilder(connString)
dataSourceBuilder.UseVector()
Dim dataSource = dataSourceBuilder.Build()
Dim conn = dataSource.OpenConnection()
Enable the extension
Using cmd As New NpgsqlCommand("CREATE EXTENSION IF NOT EXISTS vector", conn)
cmd.ExecuteNonQuery()
End Using
conn.ReloadTypes()
Create a table
Using cmd As New NpgsqlCommand("CREATE TABLE items (id serial PRIMARY KEY, embedding vector(3))", conn)
cmd.ExecuteNonQuery()
End Using
Insert a vector
Using cmd As New NpgsqlCommand("INSERT INTO items (embedding) VALUES ($1)", conn)
Dim embedding As New Vector(New Single() {1, 1, 1})
cmd.Parameters.AddWithValue(embedding)
cmd.ExecuteNonQuery()
End Using
Get the nearest neighbors
Using cmd As New NpgsqlCommand("SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", conn)
Dim embedding As New Vector(New Single() {1, 1, 1})
cmd.Parameters.AddWithValue(embedding)
Using reader As NpgsqlDataReader = cmd.ExecuteReader()
While reader.Read()
Console.WriteLine(reader.GetValue(0))
End While
End Using
End Using
Add an approximate index
Using cmd As New NpgsqlCommand("CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)", conn)
cmd.ExecuteNonQuery()
End Using
Use vector_ip_ops
for inner product and vector_cosine_ops
for cosine distance
See a full example
History
Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/pgvector/pgvector-dotnet.git
cd pgvector-dotnet
createdb pgvector_dotnet_test
dotnet test
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net6.0 is compatible. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
-
net6.0
- Npgsql (>= 8.0.3)
NuGet packages (6)
Showing the top 5 NuGet packages that depend on Pgvector:
Package | Downloads |
---|---|
Pgvector.EntityFrameworkCore
pgvector support for Entity Framework Core |
|
Microsoft.KernelMemory.MemoryDb.Postgres
Postgres(with pgvector extension) connector for Microsoft Kernel Memory, to store and search memory using Postgres vector indexing and Postgres features. |
|
Microsoft.SemanticKernel.Connectors.Postgres
Postgres(with pgvector extension) connector for Semantic Kernel plugins and semantic memory |
|
LangChain.Databases.Postgres
Postgres for LangChain. |
|
Pgvector.Dapper
pgvector support for Dapper |
GitHub repositories (5)
Showing the top 5 popular GitHub repositories that depend on Pgvector:
Repository | Stars |
---|---|
microsoft/semantic-kernel
Integrate cutting-edge LLM technology quickly and easily into your apps
|
|
dotnet/eShop
A reference .NET application implementing an eCommerce site
|
|
microsoft/kernel-memory
RAG architecture: index and query any data using LLM and natural language, track sources, show citations, asynchronous memory patterns.
|
|
thangchung/practical-dotnet-aspire
The practical .NET Aspire builds on the coffeeshop app business domain
|
|
Azure-Samples/eShopOnAzure
A variant of https://github.com/dotnet/eShop that uses Azure services
|
Version | Downloads | Last updated |
---|---|---|
0.3.0 | 227,799 | 6/26/2024 |
0.2.0 | 511,071 | 11/24/2023 |
0.2.0-rc.2 | 6,815 | 10/26/2023 |
0.2.0-rc.1 | 752 | 10/14/2023 |
0.1.4 | 98,590 | 9/25/2023 |
0.1.3 | 150,129 | 5/20/2023 |
0.1.2 | 4,637 | 4/25/2023 |
0.1.1 | 68,906 | 3/12/2023 |
0.1.0 | 909 | 3/10/2023 |